ReviewClinical rheumatology2026
Handling missing data in fibromyalgia clinical trials-considerations for the use of linear mixed models in longitudinal analyses: perspectives in rheumatology.
Review in Clinical rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo critically examine current statistical practices for handling missing data in fibromyalgia randomized controlled trials (RCTs) and to provide practical guidance for the implementation of linear mixed models (LMMs).
methodsThis methodological and narrative perspective reviews recent RCTs in fibromyalgia, highlighting common limitations in the handling of missing data and longitudinal analyses. We contrast traditional approaches, such as repeated-measures ANOVA, with LMM-based strategies within the intention-to-treat (ITT) framework. Additionally, we provide a step-by-step guide for implementing LMMs in SPSS.
resultsEvidence indicates that many fibromyalgia RCTs continue to rely on suboptimal statistical methods, including the exclusion of participants with incomplete data and the use of ANOVA-based approaches. These practices may reduce consistency with the ITT principle, decrease statistical efficiency, and contribute to potentially biased treatment effect estimates. In contrast, LMMs can incorporate partially observed longitudinal data, explicitly model within-subject correlations, and accommodate unbalanced longitudinal designs under the Missing At Random (MAR) assumption. However, their validity depends on correct model specification and the plausibility of underlying assumptions.
conclusionsThe persistent gap between methodological recommendations and analytical practices in fibromyalgia RCTs may compromise the interpretability and reliability of treatment effect estimates. LMMs may represent a more flexible and methodologically appropriate approach for longitudinal analyses involving incomplete follow-up data, particularly when aligned with study design characteristics and available statistical expertise. Improving statistical rigor in this field remains essential to support more reliable clinical interpretation and evidence-informed decision-making.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.